Conventional advanced oxidation processes (AOPs) degrade toxic organic chemicals in waste streams. An emerging AOP instead mitigates organics by polymerizing them, a pathway that could offer a more sustainable option.

Chlorophenols are prevalent pollutants in aquatic environments that often require advanced oxidation processes (AOPs) for their effective removal. Polymerization has emerged as a promising pathway within AOPs for transforming and eliminating phenolic contaminants. Understanding the early-stage polymerization dynamics of chlorophenols is critical for optimizing the treatment efficiency and minimizing secondary contamination. In this study, surface-enhanced Raman spectroscopy (SERS) was employed to in situ investigate the polymerization of chlorophenols during the initial 2 min of sulfate radical-based AOPs. Structure-dependent interactions distinguished pi-mediated physisorption of protonated chlorophenols from the covalent Au-O chemisorption of phenolates. Upon sulfate radical generation, chlorophenols were rapidly oxidized and coupled, forming polymeric radical species with enhanced surface affinity for gold nanoparticle (AuNP) surfaces. Displacement of preadsorbed citrate and chloride by these products served as a proxy for evaluating polymerization potential. Polymerization and adsorption behavior were governed by the halogenation pattern and protonation state, with higher pi-electron density correlating with greater reactivity. These findings provide mechanistic insights into radical-mediated surface transformations and offer a potential strategy for in situ AuNP surface functionalization. This work advances the understanding of polymerization kinetics during AOPs and supports the development of plasmonic nanomaterials for environmental sensing and remediation.

Abstract Conventional laboratory-based chromatographic analysis provides sensitive pesticide measurements, but its cost, labor, and analytical turnaround can constrain sampling frequency in routine field monitoring. Here, we report a surface-enhanced Raman spectroscopy (SERS) workflow for rapid, molecularly specific screening of neonicotinoids in agriculture-impacted groundwater. HCl-assisted ligand exchange on gold nanoparticle surfaces, multi-band analyte identification, and hot-spot normalization enabled reliable quantification of two representative neonicotinoids in environmental waters. Field samples were collected across Wisconsin’s Central Sands from five irrigation wells and nine groundwater-fed ditch sites. SERS detected imidacloprid (IMD) and clothianidin (CLO) at calibration-derived concentrations ranging from 0.25 to 4.02 nM, with detections below the SERS limits of detection (LOD) treated as semi-quantitative estimates. Sample processing involved filtration, acidification, and incubation with the SERS substrate, followed by rapid Raman spectra acquisition of approximately 5 min per map, with an estimated material cost of ∼$0.17 per substrate. Results were cross-validated against high-performance liquid chromatography (HPLC) measurements. For measurements above the LOD, SERS recoveries relative to HPLC ranged from 96% to 117%, demonstrating the applicability of the workflow to low-nanomolar pesticide screening in field-collected waters. This practical screen-then-confirm approach can help identify where and when definitive measurements are warranted in dynamic agriculture-impacted waters.

High-throughput detection of environmental contaminants in natural and engineered aquatic systems is crucial to safeguard public health, yet the quantitative analysis of complex contaminant mixtures remains a significant challenge. Organic contaminants like polycyclic aromatic hydrocarbons (PAHs), known to pose health risks to humans upon ingestion, often coexist as complex mixtures in the environment. Here, we develop an artificial intelligence (AI)-empowered framework coupled with surface-enhanced Raman spectroscopy (SERS) to quantitatively detect PAHs in mixtures. A spectral preprocessing algorithm, PreDe, is developed to compress SERS spectra by 99.7% while retaining key Raman features. A subsequent two-stage AI framework deploys a discriminator to prescreen PAH spectra and a classifier to demix PAHs quantitatively. The discriminator achieves 100% accuracy in rejecting non-PAH spectra (i.e., representative pesticides), even without prior exposure to these spectra during training. The quantitative performance of the classifier is associated with the compositional balance of the PAH mixtures. Among the four models tested in the classifier, the convolutional neural network (CNN) and random forest (RF) consistently deliver the highest prediction accuracy and lowest error. This SERS-AI pipeline enables the rapid prescreening and quantitative demixing of target contaminants, offering a powerful new strategy for high-throughput monitoring of contaminants in complex matrices.

Oxidation processes mediated by soil enzymes were monitored in situ and quantitatively to shed light on enzyme activity sensing.

Correction for ‘Emerging investigator series: quantitative insights into the relationship between the concentrations and SERS intensities of neonicotinoids in water’ by Shengdong Liu et al. , Environ. Sci.: Nano , 2024, 11 , 3294–3300, https://doi.org/10.1039/D4EN00221K.

The widespread use of neonicotinoid (NEO) pesticides has raised significant environmental concerns due to their toxicity. We investigated the performance of 16 nanobiochars (NBCs), including NBC produced by Douglas fir at 900 °C (Doug 900 NBC), as sustainable sorbents for removing three common NEOs from aqueous solutions: imidacloprid, clothianidin, and thiamethoxam. The NBCs showed high sorption efficiency (∼ 100 %) and fast sorption kinetics (< 0.5 h) for three NEOs at environmentally relevant concentrations (100 ng/L). The sorption efficiency of NEOs was determined by the physicochemical properties of NBCs, including specific surface area (SSA), pore volume (PV), pore diameter (PD), and elemental composition (carbon, nitrogen, and hydrogen contents). The NBCs with higher SSA and larger PV offered more abundant sorption sites, facilitating fast NEO sorption. Particularly, the Doug 900 NBC achieved ∼ 100 % removal efficiency of NEOs within 0.5 h under simulated groundwater conditions (67.5 mg/L of total dissolved solids and 10 mg/L of humic acid). The Doug 900 NBC also maintained high removal efficiency over four continuous reuse cycles. The structural equation modeling revealed that pyrolysis temperature indirectly affects NEO sorption by modifying NBC's properties of SSA, PV, and PD. Our findings highlight the high potential of NBCs for sustainable removal of NEO pesticides in aquatic environments at environmentally relevant concentrations.

Current catalytic materials and processes designed for water treatment face a significant challenge in balancing reactivity and stability. Catalysts with initially high reactivity often lack long-term stability under environmentally relevant conditions, limiting their advancement toward practical application. In this study, we demonstrate that spatial confinement of catalysts at angstrom scale can significantly enhance the stability of iron oxyfluoride (FeOF), a highly efficient catalyst for advanced oxidation. We fabricate a catalytic membrane by intercalating FeOF catalysts between layers of graphene oxides. In flow-through operation, the catalytic membrane maintains near-complete removal of model pollutants, neonicotinoids, for over two weeks by effectively activating H2O2 to generate •OH. Catalyst deactivation is significantly mitigated by spatially confining fluoride ions leached from the catalyst, which is identified as the primary cause of catalytic activity loss. The angstrom-scale membrane channels effectively reject the majority of natural organic matter via size exclusion, thereby preserving radical availability and sustaining pollutant degradation under practical conditions. This innovative strategy for enhancing catalyst stability can be potentially applied to other existing catalysts developed for water treatment applications.

Exacerbated by ongoing anthropogenic activities, inorganic arsenic (As) contamination of drinking water poses a significant health threat worldwide. Current quantification and speciation strategies rely on resource-extensive laboratory analyses. Providing rapid, low-cost, and reproducible measurements, surface-enhanced Raman spectroscopy (SERS) has emerged as a promising As detection tool in both laboratory and field settings. This study enhances understanding of SERS for As speciation using unmodified citrate-capped gold nanoparticles (cit-AuNPs). Herein, changes to the Raman fingerprints of As(III) and As(V) oxyanions are characterized across a wide pH range and under high/low dissolved oxygen concentrations. The quantification efficacy of the cit-AuNPs for As(III) and As(V) is also identified as being pH-dependent with quantifications achieved as low as 10 ppb and 1 ppm, respectively. These values are acquired near pH 8, where the highest SERS intensities are observed. Low oxygen conditions have minimal impact on the spectra, suggesting a limited role for As oxidation in its detection and speciation under ambient conditions. These findings offer novel insights into the interactions between As and cit-AuNPs across varying pH levels, and establish a foundation for deploying Au-based SERS to quantify and speciate inorganic As in environmental settings.

Neonicotinoids (neonics), including imidacloprid (IMD), clothianidin (CLO), and thiamethoxam (THI), account for over 25 % of the global pesticide market and pose significant risks to ecosystems and human health. To address the urgent need for reliable, rapid, and cost-effective monitoring of these emerging contaminants, an ultrasensitive analytical method based on optimized ligand exchange-enabled surface-enhanced Raman spectroscopy (SERS) was developed. The synergistic effects of protons and halides on the SERS detection of IMD, CLO, and THI using citrate-capped gold nanoparticles (AuNPs) were investigated. At pH 1, citrate was fully protonated and readily desorbed from AuNP surfaces, allowing neonics to adsorb and enhancing their SERS signals. Chloride ions further facilitated citrate desorption and served as molecular bridges between neonics and AuNPs. In addition to increased neonic adsorption, extensive AuNP aggregation and SERS hot spot formation were induced, leading to amplified SERS intensities. By combining protons and halides, detection sensitivity was significantly improved, enabling IMD, CLO, and THI detection at concentrations as low as 2-5 nM. This method was successfully applied to detect neonics in real soil water samples, providing a simple, highly sensitive approach for in-situ monitoring of neonicotinoids in agricultural runoff.

The detection of nanoplastics (NPs) in complex natural water systems is hindered by matrix interferences and limitations in current analytical techniques. This study presents Pre_seg, a Raman spectral processing algorithm integrated with regenerable anodic aluminum oxide (AAO) membrane sensors, for ultrasensitive, rapid, and quantitative NP detection at the single-particle level. The AAO membranes function as both filtration substrates and Raman sensors, reducing sample loss and contamination. Pre_seg incorporates statistically determined thresholds for signal-to-noise ratios (SNRs) and full width at half maximums (fwhms) across segmented spectral ranges, effectively minimizing noise and enhancing accuracy and sensitivity of NP detection. Pre_seg achieved 93.5% prediction accuracy of NPs and ≥90.4% rejection accuracy for non-NP entries. Mixed NPs were quantified at the lowest concentration of 0.5 μg L-1. The robustness of Pre_seg was validated in eutrophic and oligotrophic lake matrices following oxidation digestion pretreatment to mitigate organic interferences. Furthermore, the AAO membrane sensors demonstrated stability through multiple regeneration and reuse cycles. This innovative approach advances NP detection by enabling scalable, customizable, and environmentally relevant monitoring.

Quantification and identification of microplastics (MPs) (plastics < 5 mm) remain challenging due to the current laborious, inconsistent, and time-intensive measurement techniques. A promising time- and cost-effective alternative is staining with the fluorescent dye Nile red (NR). This review covers the wide range of NR staining methods, illumination conditions, and instrumentation for fluorescence-based detection and classification that have been developed thus far, highlighting the potential of NR fluorescence imaging to distinguish plastics. Despite notable advancements in NR staining techniques and conditions that have strengthened detection capabilities, there remains a need for further development and standardization of NR staining protocols, fluorescence imaging methods, and illumination instruments. We conduct a thorough assessment of both the advantages and limitations associated with diverse fluorescence imaging instruments, image segmentation, and classification techniques employed for detecting NR fluorescence and identifying polymer species. We also highlight critical considerations that should guide future research efforts to establish NR staining as a comprehensive, standalone method for environmental monitoring. They include investigating fluorescence behavior, especially intensity and Stokes shift, to understand the impact of solvent functional groups, plastic materials, additives and color pigments, weathering and application methods on NR sorption and fluorescence. Consideration of these factors will improve the ability to accurately identify polymer types based on their fluorescent behaviors, promoting widespread adoption of fluorescence imaging as a standalone method and enhancing cross-compatibility between NR studies.

Ensuring water quality and safety requires the effective detection of emerging contaminants, which present significant risks to both human health and the environment. Field deployable low-cost sensors provide solutions to detect contaminants at their source and enable large-scale water quality monitoring and management. Unfortunately, the availability and utilization of such sensors remain limited. This Perspective examines current sensing technologies for detecting emerging contaminants and analyzes critical barriers, such as high costs, lack of reliability, difficulties in implementation in real-world settings, and lack of stakeholder involvement in sensor design. These technical and nontechnical barriers severely hinder progression from proof-of-concepts and negatively impact user experience factors such as ease-of-use and actionability using sensing data, ultimately affecting successful translation and widespread adoption of these technologies. We provide examples of specific sensing systems and explore key strategies to address the remaining scientific challenges that must be overcome to translate these technologies into the field such as improving sensitivity, selectivity, robustness, and performance in real-world water environments. Other critical aspects such as tailoring research to meet end-users' requirements, integrating cost considerations and consumer needs into the early prototype design, establishing standardized evaluation and validation protocols, fostering academia-industry collaborations, maximizing data value by establishing data sharing initiatives, and promoting workforce development are also discussed. The Perspective describes a set of guidelines for the development, translation, and implementation of water quality sensors to swiftly and accurately detect, analyze, track, and manage contamination.

This study explores the theoretical foundation behind the application of surface-enhanced Raman spectroscopy (SERS) for neonicotinoid quantification.

Using sulfate radicals to initiate polymer production in persulfate-based advanced oxidation processes (AOPs) is an emerging strategy for organics removal. However, our understanding of this process remains limited due to a dearth of efficient methods for in situ and real time monitoring of polymerization kinetics. This study leverages plasmonic colorimetry to monitor the polymerization kinetics of an array of aromatic pollutants in the presence of sulfate radicals. We observed that the formation of polymer shells on the surfaces of gold nanoparticles (AuNPs) led to an increase and red shift in their localized surface plasmon resonance (LSPR) band as a result of an increased refractive index surrounding the AuNP surfaces. This observation aligns with Mie theory simulations and transmission electron microscopy-electron energy loss spectroscopy characterizations. Our study demonstrated that the polymerization kinetics exhibits a significant reliance on the electrophilicity and quantity of benzene rings, the concentration of aromatic pollutants, and the dosage of oxidants. In addition, we found that changes in LSPR band wavelength fit well into a pseudo-first-order kinetic model, providing a comprehensive and quantitative insight into the polymerization kinetics involving diverse organic compounds. This technique holds the potential for optimizing AOP-based water treatment by facilitating the polymerization of aromatic pollutants.

In freshwater environments, low-micrometer microplastics (LMMPs) have captured significant attention due to their prevalence and toxicity. Yet, rapid detection of LMMPs (1-10 mu m) at the single-particle level within complex freshwater matrices remains a hurdle. We developed an adaptable plasmonic membrane sensor for fast detection of individual LMMPs in eutrophic lake waters. The plasmonic membrane sensor functions both as a membrane filter and as a sensor for LMMP collection and analysis. Among the four types of membrane sensors, polycarbonate track-etch (PCTE) membrane sensors exhibit superior imaging quality for LMMPs due to their flat and homogeneous surfaces. Besides the significantly improved imaging contrast and reduced background interferences, the Raman intensity of LMMPs is enhanced by 48% +/- 25% on PCTE membrane sensors compared to unmodified membranes. The increased Raman intensities of a chemical probe with an increasing gold layer thickness and a decreasing membrane pore size suggest a surface-enhanced Raman scattering effect from the membrane sensors. The membrane sensors achieve a detection limit of 1 mu g/L and an ultrafast scanning time of 0.01 s for individual LMMPs across natural eutrophic lake water. The developed membrane sensors offer an adaptable tool for the swift and reliable detection of individual LMMPs in complex environmental matrices.

The standard methods for detecting per- and polyfluoroalkyl substances (PFAS) are precise and sensitive, but their operational complexity and high costs hinder the regular monitoring. Raman spectroscopy offers a promising complementary approach due to its fingerprinting ability for trace analysis, low operational cost, and fitness for field-deployable applications. However, the effective use of Raman spectroscopy requires a well-established Raman library, which is currently lacking. This study proposes a simple drop-coating deposition Raman (DCDR) spectroscopy method to concentrate PFAS and establish a library. We prepared DCDR samples of thirteen linear PFAS with carboxyl or sulfonic groups and seven nonfluorinated alkyl acids with similar chemical structures. Raman maps were collected using a 532 nm laser and a confocal Raman spectrometer. All tested PFAS shared common Raman bands at approximately 300, 380, and 725 cm-1, with varying band-to-band intensity ratios depending on their chain lengths, head groups, and extents of telomerization. Principal component analysis was performed on wavenumbers 200-1,000 and 1,100-1,600 cm-1 to differentiate PFAS with nonfluorinated alkyl acids and PFAS with various functional groups. To our knowledge, this research created a novel experiment-based reproducible Raman spectral library for PFAS, laying a foundation for efficient PFAS screening using Raman spectroscopy.

Emerging contaminants refer to newly discovered or previously overlooked contaminants that pose potential risks to the ecological environment and human health.They have not been included in environmental management practices or are inadequately addressed by existing management measures.Main focuses of emerging contaminants include persistent organic pollutants,endocrine disrupting chemicals,antibiotics and microplastics,among others.These contaminants have been widely detected in the environment and are considered urgent concerns for environmental safety and human health.However,compared to traditional pollutants,emerging contaminants are generally not well regulated by law-enforcement agencies.